scores-methods {cocorresp}

Get Species or Site Scores from an Ordination

Description

Function to access either species or site scores for specified axes in co-correspondence analysis ordination methods.

Usage

## S3 method for class 'predcoca'
scores(x, choices = c(1, 2),
       display = c("sites","species"), ...)

## S3 method for class 'symcoca'
scores(x, choices = c(1, 2),
       display = c("sites","species"), scaling = FALSE, ...)

Arguments

x

an ordination result

display

partial match to access scores for “sites” “species”, “loadings” or “xmatrix”. The latter two are only available for symcoca.

choices

numeric; the ordination axes to return.

scaling

logical; whether scores should be rescaled by the quarter root of the eigenvalues using rescale.symcoca.

...

arguments to be passed to other methods.

Details

Implements a scores method for symmetric co-correspondence analysis ordination results.

Value

A list with one or more components containing matrices of the requested scores:

species

A list with two components, Y and X, containing the species scores for the response matrix Y and the predictor matrix X respectively.

sites

A list with two components, Y and X, containing the site scores for the response matrix Y and the predictor matrix X respectively.

loadings

A list with two components, Y and X containing the loadings for the response and predictor matrix. For symcoca only.

xmatrix

The X matrix. For symcoca only.

Author(s)

Gavin L. Simpson, based on Matlab code by C.J.F. ter Braak and A.P. Schaffers.

References

ter Braak, C.J.F and Schaffers, A.P. (2004) Co-Correspondence Analysis: a new ordination method to relate two community compositions. Ecology 85(3), 834–846

See Also

scores, for further details on the method.

Examples

## load some data
data(beetles)
data(plants)

## log transform the bettle data
beetles <- log(beetles + 1)

## fit the model, a symmetric CoCA
bp.sym <- coca(beetles ~ ., data = plants, method = "symmetric")

## extract the scores
scr <- scores(bp.sym)

## predictive CoCA using SIMPLS and formula interface
bp.pred <- coca(beetles ~ ., data = plants)
scr2 <- scores(bp.pred)